Information in a recurrent Retina-V1 network with realistic noise, feedback and nonlinearities

TL;DR

This paper presents a comprehensive model of early visual processing that combines realistic recurrent neural networks, accurate noise modeling, and reliable information measures to study information flow and feedback effects.

Contribution

It integrates plausible recurrent networks with realistic noise and validated information measures to enable a reliable analysis of information flow in early vision.

Findings

Feedback reduces information loss along the pathway.

Optimal feedback enhances signal reconstruction accuracy.

Recurrent connections improve stability and information retention.

Abstract

Quantitative estimation of information flow in early vision with psychophysically realistic networks is still an open issue. This is because, up to date, the necessary elements (general and plausible network, accurate noise, and reliable information measures) have not been put together. As a result, previous works made different approximations that limit the generality of their results. This work combines the following elements for the first time: (1) General and plausible recurrent net: a cascade of linear+nonlinear psychophysically tuned layers [IEEE TIP.06, J.Neurophysiol.19, J.Math.Neurosci.20, Neurocomp.24], augmented to consider top-down feedback following [Nat.Neurosci.21,Neurips.22]. (2) Accurate noise in every layer, which is tuned to reproduce psychometric functions both in contrast detection and discrimination following [J.Vision 25]. (3) Reliable information measures that have been checked with analytical results, both in general [IEEE PAMI 24], and in similar visual neuroscience contexts [Neurocomp.24], and hence can be applied in this (more general) case where analytical results are difficult to obtain. The joint use of these elements allows a reliable study of information flow depending on different connectivity schemes (different nonlinearities and interactions), different noise sources, and different stimuli. Results show the benefits of feedback in two ways: (1) the information loss in the data processing inequality along the pathway is reduced by the V1 -- > LGN recurrence for values of feedback that give stable steady state solutions, and (2) the stability of the net is assessed though standard Poincar\'e analysis and we find an optimal value for the feedback in terms of the accuracy of the reconstructed signal from the cortical representation.